A machine learning classifier predicts a category, from a discrete set of i categories, to which an observation belongs. This classification is based on a training set of j observations for which the ground truth category memberships are known. A popular example of a classifier is an email spam filter that classifies incoming email messages as either spam or not spam. Given a large and diverse enough training set, such a classifier can operate with high accuracy on new email messages.